Neural Network Enhanced Single-Photon Fock State Tomography

TL;DR

This paper demonstrates a machine learning approach to perform quantum state tomography of single-photon states, enabling direct estimation of photon number distributions and phase space negativity with improved speed and robustness.

Contribution

It introduces a neural network-based method for quantum state tomography that directly estimates parameters from homodyne detector data, surpassing traditional measurement techniques.

Findings

Neural network enhances quantum state characterization accuracy.

Direct estimation of Wigner negativity is achieved.

Method is faster and more robust than conventional techniques.

Abstract

Even though heralded single-photon sources have been generated routinely through the spontaneous parametric down conversion, vacuum and multiple photon states are unavoidably involved. With machine-learning, we report the experimental implementation of single-photon quantum state tomography by directly estimating target parameters. Compared to the Hanbury Brown and Twiss (HBT) measurements only with clicked events recorded, our neural network enhanced quantum state tomography characterizes the photon number distribution for all possible photon number states from the balanced homodyne detectors. By using the histogram-based architecture, a direct parameter estimation on the negativity in Wigner's quasi-probability phase space is demonstrated. Such a fast, robust, and precise quantum state tomography provides us a crucial diagnostic toolbox for the applications with single-photon Fock states and other non-Gaussisan quantum states.